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prompt-to-scene

prompt-to-scene

by 316sandon12
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

generate_model

Create 1-3 textured GLB model candidates from a text prompt and optional reference images, then import the selected model into your scene for placement.

Instructions

Generate 1-3 textured GLB candidates, then use the existing preparation/import pipeline.

Configure providers in the local app. Meshy consumes credits: allow_paid only when user authorization covers this provider/count. Self-hosted adapter implements docs/PROVIDERS.md. Optional 1-4 PNG/JPEG references. Default candidates stay outside the scene for review; publish_prepared selects one. Saved provider IDs survive interruption without resubmission. Cancellation stops local work; an already submitted provider task may still consume credits.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "asset_id": "<asset_id>",
      "prompt": "<prompt>"
    },
    "name": "generate_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
promptYes
asset_idYes
positionNo
providerNolocal
settingsNo
allow_paidNo
image_pathsNo
preview_onlyNo
candidate_countNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.6.0

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden and does unusually well: it discloses credit consumption, that cancellation stops local work but a submitted provider task may still consume credits, that saved provider IDs survive interruption without resubmission, and that candidates default to outside the scene. It does not describe error/rejection behavior or the shape of the returned candidates.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose is front-loaded in the first sentence and the remaining clauses each carry distinct, non-redundant information. The telegraphic, line-broken style is dense but every clause earns its place; it reads slightly run-on rather than tight prose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex 9-parameter generation tool with no annotations and no output schema, the description covers cost, cancellation, and publication semantics well but omits the meaning of four parameters and gives no sense of the return payload or how candidates are surfaced for review.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% across 9 parameters, so the description must compensate. It explains provider, allow_paid, candidate_count, preview_only behavior and the 1-4 PNG/JPEG references for image_paths, but leaves asset_id, prompt, position, and settings completely unexplained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Opens with a specific verb+resource+output ('Generate 1-3 textured GLB candidates') and situates it in the pipeline, noting that publish_prepared selects one. An agent can distinguish this from siblings like prepare_asset or publish_prepared without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives clear operational context: configure providers in the local app, use allow_paid only when authorization covers the provider/count, and use publish_prepared to select a candidate. It stops short of explicit when-not-this-tool exclusions versus list_generation_providers or prepare_asset.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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